Wireless telecommunication service providers are keenly interested in providing high quality, reliable services for their customers in today's highly competitive marketplace. A significant aspect affecting the service quality is the consistency of radio coverage within cell coverage areas of the network. Moreover, an additional aim from the provider's perspective is to be able to increase capacity while maintaining quality and reliability. As known by those skilled in the art, telecommunication networks operating in accordance with direct sequence code division multiple access (DS-CDMA), which are also referred to as spread spectrum systems, the service quality is particularly affected by the number of users in the cell. This is because the inherent nature of spread spectrum systems permits all users to transmit and receive on a common frequency band thus each of the transmissions necessarily “interfere” with each other in what is known as multiple access interference (MAI).
As the number of users in the cell increase more interference is introduced causing the mobiles to transmit with increased power in order to sufficiently communicate with the base station and thereby making the problem worse. This effect tends to be more prominent on uplink transmissions from mobiles since their power levels tend to be more limited in comparison to that of the base station. Another consequence of increased interference is that the cell coverage area tends to contract, on the other hand as less traffic is present, the coverage area of the cell tends to expand. The tendency for cells to shrink and expand in relation to number of users in the cell is known as “cell breathing” and occurs, for example, since each user in a CDMA system cumulatively contributes to the interference in the cell since they simultaneously share a common frequency band.
Another type of interference that has a significant affect on CDMA systems is multipath interference. Radio channel signals between a transmitter and receiver typically do not propagate only along one path. Reflections and refractions of a signal, which are particularly acute in urban environments having many buildings and obstructions, will be received over a number of different paths which are copies of the transmitted signal, each having different amplitudes, phases, temporal delays and arrival angles. At the receiver these signals can interfere with each other, in some instances being constructive at some points and destructive at others. Still another type of interference is the so-called near-far interference which happens when a strong signal from a mobile close to the base station overwhelms a weaker signal from a distant mobile closer to the cell boundary, for example.
An advantage of CDMA systems is that spread spectrum modulation combined with the use of a Rake receiver, can be effective against multipath interference. Rake receivers are used to receive and resolve multipath signals in which multiple copies of the signal are received with varying delays. This is because the spread spectrum waveform is well matched to the multipath channel and thus CDMA signals employ the use of multipath diversity, which also reduces the effect from signal fading.
FIG. 1 shows a simplified block diagram of an exemplary direct sequence CDMA transmitter. The binary data signal is directly modulated by a discrete code valued signal that is discrete in time. The data signal is multiplied by the code signal shown by the code generator block 110 whereby the resulting signal modulates the wideband carrier shown by the wideband modulator block 120. A carrier generator 130 then modulates the wideband carrier signal 120 for transmission through antenna 140. Various code modulation techniques can be used. Often these are a form of phase shift keying such as binary phase shift keying (BPSK) or quadrature phase shift keying (QPSK), for example. The code signal is referred to as the chip rate in which one chip is equal to one symbol in the spreading code signal.
FIG. 2 shows a simplified block diagram of an exemplary direct sequence CDMA receiver. At the receiver, the signal is first down converted with the help of carrier generator 250. The signal is then coherently detected and filtered with the help of filter matching on the chip waveform that is then despread with despreader block 210. In order to despread the signals, the receiver must know the code sequence used in spreading the signal, the codes of the received signal, together with the condition that the locally generated code generated by code generator 220 must be synchronized. The code synchronization tracking block 230 performs the operation of synchronizing the signal during the entire time the signal is being received. The signal despread by despreading block 210 is fed into data demodulator block 240. The data demodulator block 240 demodulates the signal in order to allow the original data to be recovered. Recovery of the data is possible at the receiving end since the code sequences are known a priori.
The lack of spectral resources coupled with increasing demand for wireless voice and data services such as Internet access, audio, video, and multimedia applications have highlighted capacity as a critical issue. Among other things the lack of capacity has been the driving force behind the shift to wideband systems such as Wideband Code Division Multiple Access (W-CDMA). W-CDMA standard is a preferred air interface fulfilling 3G requirements for improved quality of service and capacity. It is able to provide connections of 384 bits/s for mobile applications and as much as 2 Mbits/s in stationary environments. The capacity of direct sequence DS-CDMA systems such as W-CDMA using a Rake receiver is interference limited i.e. more users in a cell creates more interference for all the other users. Since the spreading codes in signals from the users are not completely orthogonal, this results in residual interference within the cell, known as multiple access interference (MAI), where when together with interference from neighboring cells, significantly degrades performance in the cell. MAI is a major factor in limiting cell capacity and the removal of such interference would lead to a significant increase in capacity.
A conventional approach for dealing with interference is by employing a single-user matched filter in combination with Rake combiner. The users use spreading sequences of nearly uncorrelated codes so that interference from other users are treated as non-coherent interference and rejected, however this technique has been shown not to be the optimal approach. This is because the sum of the cross-correlation between codes at high loading can be significantly larger than the autocorrelation that is detected. Furthermore, the interference itself contains much information on the structure and content of the signal and can be used advantageously. In W-CDMA, multi-user detectors (MUD), also referred to as interference cancellers, provide a means for reducing the effect of multiple access interference. A multi-user detector (MUD) is an advanced detector in base stations that uses a more sophisticated approach to remove interference components from the signal. The benefit of using MUDs is that they dramatically increase system capacity. Furthermore, they can be used effectively for mitigating the effect of near-far interference that typically can plague DS-CDMA systems, by first detecting, and then subtracting the problem mobile from the input signal.
However, use of the optimum multi-user detector has not been found to be practical for implementation. This is because the complexity of the optimum detector becomes exponential to the number of users and requires computations that are too demanding for the current silicon based IC processing technology or any conventional digital technology currently employed. Thus, a number of suboptimum multi-user and interference receivers have been proposed or developed. The suboptimum receivers can be classified into two major categories: linear multi-user detectors and subtractive interference cancellers. Linear detectors apply a linear transform into the outputs of the matched filters that try to remove the MAI i.e. interference resulting from correlations between user codes. Examples of linear multi-user detectors that are most commonly referred to are decorrelating detectors, where the linear filter has a zero output, and the minimum mean square error (LMMSE) detector, where the linear filter has a minimum output energy. In subtractive interference cancellation, the MAI is estimated and then subtracted from the received signal. The cancellation can be performed with successive interference cancellation (SIC) or with parallel interference cancellation (PIC).
The successive cancellation technique requires at least several iterations for a user however the individual iterations are less complex than with the parallel cancellation technique. Furthermore, SIC does not distinguish users from one another from the spreading sequences and the canceling is performed serially in which the delay bits are added such that the complexity increases linearly with the number of users and iterations. Moreover, SIC has less computational complexity than PIC, which is more hardware intensive to process users in parallel. In a comparative sense, PIC is based on a Jacobi algorithm and thus requires specific conditions on the interference matrix for convergence. Special techniques can be used to reach convergence for particular scenario of mobile transmission but there is generally no unique solution. Moreover, PIC typically requires more iterations than SIC, which means the converge rate is usually much slower. The computational complexity is considerably larger in PIC due to the intensive parallel processing that has to be applied. In SIC, the converge rate is generally much faster and the processing flow can be serialized.
Even though the relative complexity is lower, the computational demands from SIC require processor circuits capable of producing several Giga operations per second, which is extremely challenging when using conventional integrated circuits. The serial nature of the SIC algorithm requires very fast electronics in order to process the many channels present in the multiple access interference. Furthermore, when assuming long spreading codes, the cross-correlation between the spreading codes changes from symbol to symbol instead of over several symbols, thereby increasing the complexity and required processing even further. In fact, the reason for the option of using short spreading codes in W-CDMA was to enable the future use of a multi-user detector with the projected available processing power. However, the use of long codes is preferable due to better interference averaging, and thus better performance in canceling the interference. The implementation of comprehensive SIC algorithms are currently not practical due to conventional hardware limitations and, at present, only partial cancellation methods have been considered, which have not been found to be very beneficial.
In view of the foregoing, it is desirable to provide a technically viable solution for using interference cancellation in wireless CDMA systems.